[Coursera] Practical Machine Learning by Jeff Leek, PhD, Brian Caffo, PhD, Roger D. Peng, PhD

Creation Time	March 21, 2025, 7:35 a.m.
Last Access Time	May 31, 2025, 7:41 p.m.
File Size	353.9 MB
Keywords	Machine Peng D Jeff Coursera Leek Practical Brian Roger Learning PhD by Caffo
Total Requests	4
Total Files	108
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02_Week_2/06_Covariate_creation.mp4 22.8 MB
02_Week_2/07_Preprocessing_with_principal_components_analysis.mp4 17.4 MB
04_Week_4/01_Regularized_regression.mp4 16.8 MB
03_Week_3/01_Predicting_with_trees.mp4 16.2 MB
02_Week_2/08_Predicting_with_Regression.mp4 15.8 MB
03_Week_3/05_Model_Based_Prediction.mp4 14.5 MB
02_Week_2/09_Predicting_with_Regression_Multiple_Covariates.mp4 14.5 MB
02_Week_2/04_Plotting_predictors.mp4 14.2 MB
01_Week_1/06_Types_of_errors.mp4 13.9 MB
02_Week_2/05_Basic_preprocessing.mp4 13.6 MB
01_Week_1/03_Relative_importance_of_steps.mp4 12.3 MB
03_Week_3/02_Bagging.mp4 11.4 MB
01_Week_1/05_Prediction_study_design.mp4 11.1 MB
01_Week_1/02_What_is_prediction.mp4 11.0 MB
04_Week_4/03_Forecasting.mp4 10.6 MB
01_Week_1/01_Prediction_motivation.mp4 10.5 MB
01_Week_1/08_Cross_validation.mp4 10.1 MB
04_Week_4/02_Combining_predictors.mp4 9.3 MB
03_Week_3/04_Boosting.mp4 9.1 MB
02_Week_2/03_Training_options.mp4 9.0 MB
03_Week_3/03_Random_Forests.mp4 8.7 MB
01_Week_1/04_In_and_out_of_sample_errors.mp4 8.7 MB
02_Week_2/01_Caret_package.mp4 8.2 MB
01_Week_1/09_What_data_should_you_use.mp4 7.8 MB
02_Week_2/02_Data_slicing.mp4 7.0 MB
01_Week_1/07_Receiver_Operating_Characteristic.mp4 6.1 MB
04_Week_4/04_Unsupervised_Prediction.mp4 5.4 MB
02_Week_2/01_Caret_package.pdf 2.3 MB
02_Week_2/06_Covariate_creation.pdf 1.9 MB
01_Week_1/02_What_is_prediction.pdf 1.8 MB
02_Week_2/08_Predicting_with_Regression.pdf 1.8 MB
02_Week_2/09_Predicting_with_Regression_Multiple_Covariates.pdf 1.8 MB
04_Week_4/03_Forecasting.pdf 1.8 MB
04_Week_4/02_Combining_predictors.pdf 1.7 MB
02_Week_2/07_Preprocessing_with_principal_components_analysis.pdf 1.7 MB
01_Week_1/06_Types_of_errors.pdf 1.7 MB
04_Week_4/01_Regularized_regression.pdf 1.7 MB
01_Week_1/09_What_data_should_you_use.pdf 1.7 MB
02_Week_2/04_Plotting_predictors.pdf 1.7 MB
03_Week_3/01_Predicting_with_trees.pdf 1.6 MB
03_Week_3/02_Bagging.pdf 1.5 MB
02_Week_2/05_Basic_preprocessing.pdf 1.5 MB
03_Week_3/03_Random_Forests.pdf 1.3 MB
02_Week_2/03_Training_options.pdf 1.3 MB
03_Week_3/05_Model_Based_Prediction.pdf 1.2 MB
01_Week_1/01_Prediction_motivation.pdf 1.2 MB
03_Week_3/04_Boosting.pdf 1.2 MB
01_Week_1/04_In_and_out_of_sample_errors.pdf 1.0 MB
01_Week_1/03_Relative_importance_of_steps.pdf 906.8 KB
01_Week_1/05_Prediction_study_design.pdf 861.7 KB
02_Week_2/02_Data_slicing.pdf 832.7 KB
04_Week_4/04_Unsupervised_Prediction.pdf 735.8 KB
01_Week_1/08_Cross_validation.pdf 501.2 KB
01_Week_1/07_Receiver_Operating_Characteristic.pdf 335.6 KB
02_Week_2/06_Covariate_creation.srt 29.6 KB
02_Week_2/07_Preprocessing_with_principal_components_analysis.srt 24.3 KB
04_Week_4/01_Regularized_regression.srt 21.2 KB
03_Week_3/01_Predicting_with_trees.srt 20.5 KB
02_Week_2/08_Predicting_with_Regression.srt 19.1 KB
03_Week_3/05_Model_Based_Prediction.srt 19.0 KB
01_Week_1/06_Types_of_errors.srt 18.8 KB
02_Week_2/05_Basic_preprocessing.srt 18.5 KB
01_Week_1/03_Relative_importance_of_steps.srt 18.4 KB
02_Week_2/06_Covariate_creation.txt 18.4 KB
02_Week_2/09_Predicting_with_Regression_Multiple_Covariates.srt 18.2 KB
02_Week_2/04_Plotting_predictors.srt 18.0 KB
01_Week_1/05_Prediction_study_design.srt 16.3 KB
01_Week_1/01_Prediction_motivation.srt 15.1 KB
01_Week_1/02_What_is_prediction.srt 15.1 KB
01_Week_1/08_Cross_validation.srt 15.1 KB
02_Week_2/07_Preprocessing_with_principal_components_analysis.txt 15.0 KB
03_Week_3/02_Bagging.srt 14.7 KB
04_Week_4/01_Regularized_regression.txt 13.0 KB
02_Week_2/03_Training_options.srt 12.6 KB
03_Week_3/01_Predicting_with_trees.txt 12.6 KB
01_Week_1/04_In_and_out_of_sample_errors.srt 12.4 KB
04_Week_4/03_Forecasting.srt 12.2 KB
02_Week_2/08_Predicting_with_Regression.txt 11.9 KB
01_Week_1/06_Types_of_errors.txt 11.7 KB
03_Week_3/04_Boosting.srt 11.6 KB
03_Week_3/05_Model_Based_Prediction.txt 11.6 KB
03_Week_3/03_Random_Forests.srt 11.5 KB
02_Week_2/05_Basic_preprocessing.txt 11.5 KB
01_Week_1/09_What_data_should_you_use.srt 11.4 KB
01_Week_1/03_Relative_importance_of_steps.txt 11.3 KB
02_Week_2/09_Predicting_with_Regression_Multiple_Covariates.txt 11.3 KB
02_Week_2/04_Plotting_predictors.txt 11.2 KB
02_Week_2/01_Caret_package.srt 11.0 KB
04_Week_4/02_Combining_predictors.srt 10.9 KB
01_Week_1/05_Prediction_study_design.txt 10.2 KB
01_Week_1/02_What_is_prediction.txt 9.4 KB
02_Week_2/02_Data_slicing.srt 9.4 KB
01_Week_1/08_Cross_validation.txt 9.3 KB
01_Week_1/01_Prediction_motivation.txt 9.3 KB
03_Week_3/02_Bagging.txt 9.1 KB
01_Week_1/07_Receiver_Operating_Characteristic.srt 8.6 KB
01_Week_1/04_In_and_out_of_sample_errors.txt 7.8 KB
02_Week_2/03_Training_options.txt 7.7 KB
04_Week_4/03_Forecasting.txt 7.7 KB
03_Week_3/04_Boosting.txt 7.2 KB
03_Week_3/03_Random_Forests.txt 7.2 KB
04_Week_4/04_Unsupervised_Prediction.srt 7.2 KB
01_Week_1/09_What_data_should_you_use.txt 7.1 KB
02_Week_2/01_Caret_package.txt 6.9 KB
04_Week_4/02_Combining_predictors.txt 6.7 KB
02_Week_2/02_Data_slicing.txt 5.9 KB
01_Week_1/07_Receiver_Operating_Characteristic.txt 5.4 KB
04_Week_4/04_Unsupervised_Prediction.txt 4.5 KB

[Coursera] Practical Machine Learning by Jeff Leek, PhD, Brian Caffo, PhD, Roger D. Peng, PhD

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